Workforce Analysis in the Eagle Ford Shale

Report for the Community Adjustment and Investment Program: Workforce Analysis in the Eagle Ford Shale May 2011 Prepared by: Center for Community and Business Research The University of Texas at San Antonio Institute for Economic Development

This study was performed by The University of Texas at San Antonio Institute for Economic Development s Center for Community and Business Research. The project was supported with funding from the North American Development Bank s Community Adjustment and Investment Program (CAIP). Any findings, conclusions or opinions are those of the authors and not necessarily those reflected by The University of Texas at San Antonio or the North American Development Bank. Acknowledgments: This report was prepared by Dominique Halaby, Javier Oyakawa and Christine Shayne. A special thanks to Sylvia Gaona with CAIP and the entire NADBank organization for their support and assistance. 2

Workforce Analysis in the Eagle Ford Shale Table of Contents Introduction... 4 Manufacturing and Natural Resources Industries... 4 Manufacturing Industry... 5 Natural Resources Impacts... 7 Eagle Ford Shale... 9 Employment Opportunities... 10 County Impacts... 13 La Salle County... 13 Other Counties... 14 Conclusion... 15 Appendix B: Alternative Methods for Assessing the Counties most Impacted by NAFTA... 20 Appendix C: County Oil and Gas Production 2007-2010... 27 Appendix D: County Unemployment Rates 7/2007-1/2011... 28 3

Introduction The North American Free Trade Agreement (NAFTA), implemented in 1994, is generally understood to have had a number of significant effects relating to variables such as employment, the flow of goods through North America, and even the health of the environment. The intent of the agreement was to encourage competition and open the lines of trade between the United States, Canada and Mexico. While it is generally agreed that this effort has been successful, there is strong indication that certain regions of the United States have been negatively affected in the area of employment. The number of jobs affected is highly contested; however, certain states and counties are considered to have been particularly high-loss employment areas. Texas provides a particularly interesting case for study. Sharing approximately 1,250 miles of border with Mexico, this region accounts for almost two-thirds of the total length of the US-Mexico border. With such close proximity to less expensive labor in Mexico and with the cost reductions associated with producing in Mexico and trading into the United States resulting from NAFTA, it is of particular importance to address the private sector losses in this state. For the purposes of this analysis, the study area is confined to the 79-county service area of the South-West Texas Border Small Business Development Center (SBDC) Network. This region spans the entire Texas-Mexico border and contains all 30 Texas counties marked as Designated Eligible Areas (DEA) by the North American Development Bank s Community Adjustment Investment Program. Manufacturing and Natural Resources Industries According to the Economic Policy Institute, Manufacturing and Natural Resources/Mining industries were substantially more negatively affected by NAFTA than any other industries between 1993 and 2000 (See Table 1). 4

Industry Code Table 1. NAFTA Employment Loss 1993-2000 Industry 1993 Texas non-farm Employment* EPI-Estimated Job Losses due to NAFTA** % of TX Jobs Lost due to NAFTA 10 Total, All Industries 7,488,000 41,067 0.55% 1011 Natural Resources and Mining Rank 155,100 1,998 1.29% 2 1012 Construction 355,100 251 0.07% 7 1013 Manufacturing 941,200 26,080 2.77% 1 1021 Trade, Transportation and Utilities 1,624,800 3,399 0.21% 5 1022 Information 177,800 344 0.19% 6 1023 Financial Activities 456,800 1,104 0.24% 4 1024, 1025, 1026, 1027 Services 2,401,200 7,757 0.32% 3 1028 Public Administration 1,376,000 135 0.01% 8 *http://www.tracer2.com/cgi/dataanalysis/areaselection.asp?tablename=ces **http://www.epi.org/page/-/old/briefingpapers/nafta01/state-losses-table.pdf Manufacturing Industry The United States manufacturing industry is generally accepted to have taken the hardest employment hit by NAFTA, as companies have spent almost two decades since the agreement s inception struggling to remain competitive with the lower costs of labor and other advantages held by manufacturing firms outside of the US, most especially in Mexico. The Economic Policy Institute, identifying a high concentration in manufacturing industries such as motor vehicles, textiles, and electrical appliances as the main cause behind the major losses in these areas, has listed the most heavily impacted states as: 1 - California (82,354 jobs) - Michigan (46,817 jobs) - New York (46,210 jobs) - Texas (41,067 jobs) - Ohio (37,694 jobs) In the 79-county study area, the manufacturing industry remains relatively more important to ten of the counties of the region (Calhoun, Lavaca, Guadalupe, Gonzales, DeWitt, McCulloch, Lampasas, Fayette, San Patricio and Gillespie) than to the rest of the state. The 20 counties with the highest location quotients (relative importance) in the manufacturing industry are depicted in Table 2. 1 http://www.epi.org/pages/briefingpapers_nafta01_impactstates/ 5

Table 2. Highest LQs for Manufacturing, 4Q2009 2009 Location Quotient: Manufacturing County Seat Calhoun 3.56 Port Lavaca Lavaca 2.78 Hallettsville Guadalupe 2.13 Seguin Gonzales 2.07 Gonzales DeWitt 1.76 Cuero McCulloch 1.75 Brady Lampasas 1.67 Lampasas Fayette 1.31 La Grange San Patricio 1.10 Sinton Gillespie 1.04 Fredericksburg Lee 0.99 Giddings Kendall 0.98 Boerne Nueces 0.97 Corpus Christi Hays 0.97 San Marcos Bastrop 0.97 Bastrop Tom Green 0.94 San Angelo Victoria 0.92 Victoria Burnet 0.91 Burnett Comal 0.85 New Braunfels Kimble 0.84 Junction These counties are primarily located on the easternmost section of the study region. The 20 counties mentioned in Table 1 are illustrated in Figure 1 and are depicted in yellow. 6

Figure 1. Map of 20 counties with highest LQ in manufacturing in SBDC region Natural Resources Impacts Within the study area, the natural resources and mining industry plays a much more significant role in the county s economy than in other part of the state. Consequently, several counties in the region have very high location quotients (relative importance) in the industry (See Table 3). 7

Table 3. Highest LQs for Natural Resources & Mining, 4Q2009 2009 Location Quotient: Nat. Res./Min. County Seat Kenedy 21.44 Sarita Irion 17.10 Mertzon Sutton 12.66 Sonora La Salle 9.88 Cotulla Pecos 9.53 Fort Stockton McMullen 8.27 Tilden Duval 7.89 San Diego Crockett 7.55 Ozona Zapata 7.39 Zapata Dimmit 7.08 Carrizo Springs Gonzales 6.84 Gonzales Jim Wells 6.74 Alice Schleicher 6.18 Eldorado Hudspeth 5.85 Sierra Blanca Refugio 5.38 Refugio Culberson 5.16 Van Horn Frio 4.79 Pearsall Brooks 4.58 Falfurrias Reeves 4.23 Pecos San Saba 4.06 San Saba Unlike in the manufacturing industry, the 20 counties with the highest location quotients in the natural resources and mining industry are more evenly dispersed throughout the 79-county region (See Figure 2). 8

Figure 2. Map of 20 counties with highest LQ in natural resources/mining in SBDC region Of the 20 counties in the region with the highest location quotient in natural resources and mining, 12 counties (Kenedy, Sutton, La Salle, Pecos, Duval, Crockett, Zapata, Dimmit, Hudspeth, Culberson, Frio and Brooks) are within the DEA. These counties, each with areas inside 65 miles from the border, are understood to have been negatively impacted by trade in terms of employment since the establishment of NAFTA, and some of these counties are still losing employment in the manufacturing sector. In attempting to identify the counties in the service area most negatively affected by NAFTA, four counties (La Salle, Zapata, Dimmit and Frio) rank amongst the top 20 counties with a high degree of dependence on industries most negatively affected by NAFTA (Appendix A). Furthermore, depending on the methodology utilized to assess possible continued impacts of NAFTA, Webb County is among the counties with the largest negative percentage change in location quotients from 2004 to 2009, signaling possible continued losses associated with NAFTA and globalization (Appendix B). Each of these counties are not only a part of the DEA they also reside in the Eagle Ford Shale region. Eagle Ford Shale The discovery of the Eagle Ford Shale in 2008 has lead to a wealth of potential opportunities for transitioning displaced manufacturing workers and others who have been affected by NAFTA into opportunities exploring and extracting natural gas resources locally. The shale area, extending through south and west Texas, is a monumental find with estimated 2010 total economic impacts of 9

$2.9 billion and support of 12,601 full-time jobs. These numbers will grow dramatically each year as activity increases in the area, with estimated 2020 economic impact for the entire shale area of $21.5 billion dollars. Six counties from the Eagle Ford Shale region overlap with the Designated Eligible Area counties: Maverick, Zavala, Frio, Dimmit, La Salle, and Webb Counties. Figure 3 shows the intersection of the DEA counties (highlighted in red) and the active Eagle Ford Shale counties (highlighted in blue). Figure 3. Map of DEA counties and Eagle Ford Shale Area These six counties overlapping accounted for close to 47 percent of the total output and employment associated with the Eagle Ford Shale in 2010. Total combined economic output for 2020 (in 2010 dollars) in the six counties is expected to be $10.1 billion. Employment Opportunities Employment effects are expected not only in natural gas extraction industries, but also in a number of relevant support industries. In total, 31,705 new jobs are projected to be supported in 2020 in the six-county area. In 2009, the total employment in the six-county area was just under 82,000. Of these expected increases, only about 12 percent of the jobs are in construction and extracting; major support categories include office and administrative support, transportation (includes the moving of goods), business and financial services, architecture and engineering, and even the sciences (Figure 4). 10

Figure 4. Eagle Ford Shale DEA Occupational Categories The average wage among employees in these six counties in 2009 was $26,730. One clear benefit to the introduction of Eagle Ford Shale activity is the number of higher-wage jobs that will become available locally as a result of this activity. Among those occupations expected to contribute more than 100 jobs, more than 80 percent are in industries with average wages higher than the average wage for the six counties. The largest number of jobs with higher wages are listed in the figure below with their corresponding average salaries nationally and the expected number of jobs to be introduced to the six-county area. 2 Accountants and auditors alone are expected to comprise nearly 1,000 jobs in the area (See Figure 5). 2 Bureau of Labor Statistics 11

Figure 5. Employment by Categories These key industries may serve as a blueprint for determining what job training opportunities should be sought out by individuals locally, especially those who may have been displaced by shifts in manufacturing and similar industries due to NAFTA. 12

County Impacts La Salle County The Eagle Ford Shale phenomenon started in La Salle County with its discovery in Hawkville Field by Petrohawk Energy Corporation. Since that initial discovery in 2008, La Salle County has been experiencing enormous gains in the production of oil, natural gas and condensate (See Table 4). Table 4. Natural Resource Production in La Salle County 2007-2010 Date Oil (BBL) GW Gas (MCF) Condensate (BBL) 2007 149,080 14,664,591 184,396 2008 165,449 13,885,440 152,490 2009 117,342 23,363,586 280,270 2010 337,099 38,419,439 530,254 As production continues to increase, so too does the need for employees in the Natural Resources and Mining Sector (See Figure 6). Figure 6. La Salle County Natural Resources and Mining Employment The annual unemployment rate of La Salle County, which peaked in 2009 at the height of the economic recession, has also begun to decline following the beginning of development of the Eagle Ford Shale area in the county. Focus must be placed toward the development of job skills in the local area so that local displaced workers may be successfully transitioned into new positions as they become available. 13

Figure 7. La Salle Unemployment Rate 7/2007 1/2011 Other Counties Similar to La Salle County, increases in natural resource production in Frio, Dimmit and Webb counties appear to correspond to a steady decline in the county s unemployment rate (Appendix C and D). However, in Maverick and Zavala counties the production of natural resources has actually diminished. Therefore, it should be of no surprise to see a steady increase in the county s unemployment rate. There are several possible reasons for the continued increase in unemployment in these two counties and more exhaustive statistical analysis of the region would be necessary to effectively establish a correlation between the region s unemployment rate and the Eagle Ford Shale. However, a cursory review of some variables may indicate that the growth in labor force in both of these counties has outpaced employment growth. Also, despite being located in the Eagle Ford Shale window, the majority of activity in Maverick and Zavala counties has been limited to speculation of future production as oppossed to actual drilling activity. This can be seen through a sharp increase in drilling permits and leashold acquisitions. As drilling activity materializes, it is likely that related employment activities will rise and the unemployment rate will correspondingly decline. 14

Conclusion The Eagle Ford Shale is already having a sizable impact on several counties in the North American Development Bank s Designated Eligible Area. Since its initial discovery in 2008, several counties have already experienced significant drops in their unemployment rate. With that said, there lies a need for continued research to isolate the relationship between the production of natural resources in the region and the county s unemployment rate. With over 31,000 jobs in a six-county area projected to be impacted by the Eagle Ford Shale in 2020, such research will greatly assist local, state and national policymakers in assessing the costs and benefits associated with allocating resources in the region. Currently, there are several entities working to assist area residents and businesses in capitalizing on the opportunities associated with the shale. Two initiatives that appear to be particularly noteworthy are being spearheaded by Coastal Bend College and the University of Texas at San Antonio. Coastal Bend College has developed a new Associates of Applied Science Degree in Oil and Gas Technology. The program was created with the assistance of several key firms in the oil and natural gas industry and was developed to assist local residents in getting the training they need to work in the field. Coastal Bend College has partnered with Pioneer Natural Resources to develop a Petroleum Industry Training program. The new program will provide safety training for area employees of Pioneer Natural Resources. The University of Texas at San Antonio s Institute for Economic Development serves as the host institution for the South-West Texas Border SBDC Network (SWT SBDC). The SWT SBDC s Rural Business Program has partnered with the SWT SBDC s International Trade Center to develop an on-line tool for aiding local area businesses in assessing opportunities for them to provide goods and services to the many oil and natural gas firms that will be in need of services. The new program will be delivered through the existing www.sbdcglobal.com platform and serve as a springboard for creating a line of services specifically tailored to the Eagle Ford Shale region. These initiatives are just two examples of programs seeking to assist the region s residents and businesses in capitalizing on the Eagle Ford Shale activity to offset any residual negative effects associated with NAFTA. With additional research to determine the relationship between production and unemployment, these entities and others will also be able to structure training programs that will be in the highest demand, assist local businesses in tailoring their services to meet the needs of oil and natural gas firms and streamline the supply-chain process by which large oil and natural gas firms employ for selecting area suppliers. Such improvements will inevitably enable local residents to get the 15

training they need to secure high-skilled, high-wage jobs in the natural resources industry, enable local firms the opportunity to capitalize on new business opportunities, and enable oil and gas firms to select the most economically competitive firm for certain goods and services. 16

Appendix A: Identification of Industries & Communities Negatively Affected by NAFTA 4Q2009 Location Quotients by County 79 Southwest TX Border Region Counties: Manufacturing Industry and Natural Resources & Mining Industry 3 Because the Manufacturing industry was affected 2.15 times as strongly as the Natural Resources & Mining industry, the LQ will be more strongly weighted for this industry. Below, Location Quotients in Manufacturing and Natural Resources & Mining are provided for each county in the Southwest Texas Border Region. The Index is calculated by multiplying the Manufacturing LQ by 2.15 and the multiplying the Natural Resources & Mining Index by 1.0. The Total Index is the sum of the two indexes. Location Quotient: Manufacturing Location Quotient: Nat. Res./Min. Manufacturing Index Nat. Res./Mining Index TOTAL INDEX Aransas 0.10 1.83 0.22 1.83 2.05 Atascosa 0.39 3.67 0.85 3.67 4.52 Bandera 0.10 1.71 0.21 1.71 1.92 Bastrop 0.97 1.18 2.08 1.18 3.26 Bee 0.16 1.98 0.35 1.98 2.33 Bexar 0.57 0.18 1.22 0.18 1.40 Blanco 0.36 3.20 0.77 3.20 3.97 Brewster 0.21 1.07 0.46 1.07 1.53 Brooks 0.00 4.58 0.00 4.58 4.58 Burnet 0.91 0.31 1.95 0.31 2.26 Caldwell 0.48 1.73 1.03 1.73 2.76 Calhoun 3.56 1.24 7.65 1.24 8.89 Cameron 0.61 0.19 1.31 0.19 1.49 Comal 0.85 0.39 1.83 0.39 2.22 Concho 0.00 2.13 0.00 2.13 2.13 Crockett 0.00 7.55 0.00 7.55 7.55 Culberson 0.26 5.16 0.55 5.16 5.71 DeWitt 1.76 0.96 3.78 0.96 4.73 Dimmit 0.11 7.08 0.24 7.08 7.33 Duval 0.00 7.89 0.00 7.89 7.89 Edwards 0.00 3.90 0.00 3.90 3.90 El Paso 0.78 0.14 1.68 0.14 1.82 Fayette 1.31 3.25 2.82 3.25 6.06 Frio 0.10 4.79 0.21 4.79 5.00 Gillespie 1.04 0.32 2.24 0.32 2.56 Goliad 0.00 2.84 0.00 2.84 2.84 Gonzales 2.07 6.84 4.44 6.84 11.28 Guadalupe 2.13 0.39 4.57 0.39 4.97 Hays 0.97 0.22 2.09 0.22 2.31 Hidalgo 0.36 1.02 0.76 1.02 1.79 3 http://www.tracer2.com/cgi/dataanalysis/areaselection.asp?tablename=industry 17

Hudspeth 0.00 5.85 0.00 5.85 5.85 Irion 0.38 17.10 0.81 17.10 17.91 Jackson 0.00 1.83 0.00 1.83 1.83 Jeff Davis 0.00 0.00 0.00 0.00 0.00 Jim Hogg 0.30 2.71 0.64 2.71 3.35 Jim Wells 0.26 6.74 0.57 6.74 7.31 Karnes 0.75 1.35 1.62 1.35 2.96 Kendall 0.98 0.30 2.10 0.30 2.41 Kenedy 0.00 21.44 0.00 21.44 21.44 Kerr 0.56 0.28 1.21 0.28 1.48 Kimble 0.84 0.66 1.80 0.66 2.46 Kinney 0.00 1.70 0.00 1.70 1.70 Kleberg 0.21 2.14 0.45 2.14 2.59 La Salle 0.00 9.88 0.00 9.88 9.88 Lampasas 1.67 0.40 3.58 0.40 3.99 Lavaca 2.78 1.23 5.97 1.23 7.20 Lee 0.99 3.24 2.13 3.24 5.37 Live Oak 0.00 3.63 0.00 3.63 3.63 Llano 0.33 0.32 0.72 0.32 1.03 Loving 0.00 0.00 0.00 0.00 0.00 Mason 0.14 2.80 0.29 2.80 3.10 Maverick 0.40 0.63 0.87 0.63 1.50 McCulloch 1.75 0.91 3.75 0.91 4.66 McMullen 0.00 8.27 0.00 8.27 8.27 Medina 0.35 1.49 0.76 1.49 2.25 Menard 0.00 2.76 0.00 2.76 2.76 Nueces 0.97 0.95 2.10 0.95 3.05 Pecos 0.06 9.53 0.14 9.53 9.67 Presidio 0.00 0.00 0.00 0.00 0.00 Real 0.26 0.89 0.56 0.89 1.45 Reeves 0.00 4.23 0.00 4.23 4.23 Refugio 0.00 5.38 0.00 5.38 5.38 San Patricio 1.10 1.69 2.36 1.69 4.05 San Saba 0.37 4.06 0.79 4.06 4.85 Schleicher 0.00 6.18 0.00 6.18 6.18 Starr 0.07 0.61 0.14 0.61 0.75 Sutton 0.00 12.66 0.00 12.66 12.66 Terrell 0.00 3.76 0.00 3.76 3.76 Tom Green 0.94 0.99 2.03 0.99 3.02 Travis 0.80 0.11 1.72 0.11 1.84 Uvalde 0.53 2.87 1.13 2.87 4.00 Val Verde 0.00 0.26 0.00 0.26 0.26 Victoria 0.92 2.05 1.98 2.05 4.03 Webb 0.16 0.64 0.34 0.64 0.98 Willacy 0.20 3.64 0.43 3.64 4.07 Williamson 0.58 0.27 1.24 0.27 1.51 Wilson 0.60 0.55 1.29 0.55 1.85 18

Zapata 0.13 7.39 0.27 7.39 7.66 Zavala 0.00 2.23 0.00 2.23 2.23 19

Appendix B: Alternative Methods for Assessing the Counties most Impacted by NAFTA ---------------------------------------------------------------------------------------------------------------------- Method 1: Summed Weighted 4Q2009 LQs From the 4Q2009 Total Index analysis alone, the twenty counties with the highest total indexes were selected: Total Index County Seat Kenedy County 21.44 Sarita Irion County 17.91 Mertzon Sutton County 12.66 Sonora Gonzales County 11.28 Gonzales La Salle County 9.88 Cotulla Pecos County 9.67 Fort Stockton Calhoun County 8.89 Port Lavaca McMullen County 8.27 Tilden Duval County 7.89 San Diego Zapata County 7.66 Zapata Crockett County 7.55 Ozona Dimmit County 7.33 Carrizo Springs Jim Wells County 7.31 Alice Lavaca County 7.20 Hallettsville Schleicher County 6.18 Eldorado Fayette County 6.06 La Grange Culberson County 5.71 Van Horn Refugio County 5.38 Refugio Lee County 5.37 Giddings Frio County 5.00 Pearsall 20

Method 2: Summed Weighted Percent Change in LQ, 4Q2004 4Q2009 The following counties had the largest negative percentage change in weighted location quotients from 4Q2004 to 4Q2009, a five-year period. LQ % Change County Seat Loving -100.0% Mentone Presidio -100.0% Marfa Val Verde -79.8% Del Rio Kimble -54.7% Junction Hudspeth -43.7% Sierra Blanca Zavala -41.4% Crystal City Willacy -35.4% Raymondville Starr -34.7% Rio Grande City Hidalgo -32.5% Edinburg Mason -29.4% Mason Jim Hogg -28.4% Hebbronville Webb -28.2% Laredo Real -28.0% Leakey San Patricio -26.8% Sinton Williamson -26.0% Georgetown Comal -24.7% New Braunfels Jackson -22.9% Edna Concho -22.6% Paint Rock Uvalde -21.7% Uvalde Duval -21.0% San Diego 21

Method 2A: Non-Weighted Percentage Change in LQ, 4Q2004 4Q2009 The following counties had the largest negative percentage change in non-weighted location quotients from 4Q2004 to 4Q2009, a five-year period. % Change LQ County Seat Loving -100.0% Mentone Presidio -100.0% Marfa Val Verde -68.4% Del Rio Kimble -55.1% Junction Hudspeth -43.7% Sierra Blanca Zavala -41.4% Crystal City Starr -37.7% Rio Grande City Willacy -37.1% Raymondville Hidalgo -34.0% Edinburg Real -33.2% Leakey Jim Hogg -32.1% Hebbronville Comal -29.9% New Braunfels Webb -28.5% Laredo Uvalde -26.0% Uvalde San Patricio -25.3% Sinton Williamson -25.2% Georgetown Cameron -24.8% Brownsville McCulloch -23.0% Brady Jackson -22.9% Edna Concho -22.6% Paint Rock 22

Method 3: Change in LQs, 4Q2004 to 4Q2009 Manufacturing ONLY The following counties had the largest negative percentage change in manufacturing location quotients from 4Q2004 to 4Q2009, a five-year period. % change LQ Manuf. County Seat Goliad -100.0% Goliad Presidio -100.0% Marfa Sutton -100.0% Sonora Val Verde -100.0% Del Rio Mason -78.6% Mason Pecos -56.5% Fort Stockton Kimble -54.2% Junction Medina -48.4% Hondo Bee -47.6% Beeville Aransas -46.7% Rockport Bandera -45.7% Bandera San Patricio -29.8% Sinton Williamson -27.1% Georgetown Hidalgo -27.1% Brownsville Webb -26.5% Laredo Frio -22.0% Pearsall El Paso -19.9% El Paso Lavaca -17.6% Hallettsville Comal -16.8% New Braunfels Wilson -16.7% Floresville 23

Method 4: Change in LQs 4Q2004 to 4Q2009 Natural Resources/Mining ONLY The following counties had the largest negative percentage change in natural resources and mining location quotients from 4Q2004 to 4Q2009, a five-year period. % change LQ Nat. Res. County Seat Loving -100.0% Mentone McCulloch -69.7% Brady Kimble -56.1% Junction Cameron -52.0% Brownsville Comal -47.9% New Braunfels Hudspeth -43.7% Sierra Blanca Zavala -41.4% Crystal City Starr -40.7% Rio Grande City Real -39.5% Leakey Willacy -38.7% Raymondville Val Verde -38.0% Del Rio Hidalgo -36.1% Edinburg Jim Hogg -35.7% Hebbronville Karnes -33.8% Karnes City Victoria -33.2% Victoria Uvalde -30.7% Uvalde Burnet -29.4% Burnet Webb -29.0% Laredo Wilson -27.2% Floresville Caldwell -25.5% Lockhart 24

Method 5: Percentage Employment Change in Manufacturing & Natural Resources/Mining, 4Q2004 4Q2009 These counties had the highest percentage loss in employment in the Manufacturing & Natural Resources/Mining industries. % Change Employment in Manuf. & Nat. Res. County Seat 2004-2009 Loving -100.0% Mentone Presidio -100.0% Marfa Val Verde -84.4% Del Rio Kimble -62.3% Junction Zavala -39.4% Crystal City Mason -33.6% Mason Medina -30.5% Hondo San Patricio -29.6% Sinton El Paso -27.7% El Paso Lavaca -27.3% Hallettsville Hidalgo -25.6% Edinburg Webb -23.6% Laredo Uvalde -22.7% Uvalde Wilson -22.6% Floresville Duval -22.5% San Diego Concho -22.0% Paint Rock Cameron -21.0% Brownsville Travis -20.8% Austin Willacy -20.4% Raymondville Kerr -20.1% Kerrville 25

Method 6: Total Employment Loss in Manufacturing and Natural Resources/Mining, 4Q2004 4Q2009 These counties had the highest loss in the number of employees in the manufacturing and natural resources/mining industries during this 5-year period. # Change Employment Manuf. & Nat. Res. 2004-2009 County Seat Travis -9907 Austin El Paso -6841 El Paso Hidalgo -4108 Edinburg Bexar -2914 San Antonio Cameron -1765 Brownsville San Patricio -960 Sinton Williamson -901 Georgetown Webb -766 Laredo Victoria -691 Victoria Val Verde -593 Del Rio Lavaca -560 Hallettsville Comal -533 New Braunfels Tom Green -376 San Angelo Uvalde -309 Uvalde Medina -234 Hondo Kerr -228 Kerville Fayette -224 La Grange Guadalupe -201 Seguin Kimble -200 Junction Duval -168 San Diego 26

Appendix C: County Oil and Gas Production 2007-2010 Date Oil (BBL) DIMMIT GW Gas (MCF) Condensate (BBL) 2007 1,090,753 2,716,886 27,635 2008 935,950 2,767,248 25,530 2009 808,029 2,979,786 163,803 2010 1,505,148 9,650,268 950,835 FRIO Date Oil (BBL) GW Gas (MCF) Condensate (BBL) 2007 523,282 834,767 1,368 2008 607,483 1,162,643 2,631 2009 547,743 1,236,933 3,589 2010 579,725 1,245,627 2,784 WEBB Date Oil (BBL) GW Gas (MCF) Condensate (BBL) 2007 126,325 221,970,167 984,637 2008 123,443 215,563,442 1,037,437 2009 116,787 202,527,276 1,016,881 2010 113,748 213,973,976 2,071,687 MAVERICK Date Oil (BBL) GW Gas (MCF) Condensate (BBL) 2007 1,727,186 2,909,732 18,776 2008 1,952,546 2,866,577 14,557 2009 1,477,017 2,298,235 10,863 2010 1,061,097 2,836,845 11,852 ZAVALA Date Oil (BBL) GW Gas (MCF) Condensate (BBL) 2007 1,062,150 836,281 40 2008 722,995 703,350 9 2009 464,337 678,875 117 2010 422,116 654,531 44 27

Appendix D: County Unemployment Rates 7/2007-1/2011 28

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About the Center for Community and Business Research The Center for Community and Business Research (CCBR) is one of ten centers within the University of Texas at San Antonio s Institute for Economic Development. Each center is specifically designed to address different economic, community, and small to medium sized business development needs. CCBR conducts regional evaluation, assessment, and long-term applied research on issues related to community and business development. CCBR serves the needs of economic development agencies, workforce development boards, businesses, associations, city, state and federal governments and other community stakeholders in search of information to make better informed decisions. CCBR develops, conducts, and reports on research projects that shed light on how organizations, communities, or the economy work. This is done through the use of various techniques including, but not limited to: Economic impact analyses Feasibility studies Surveys of business and community organizations Analysis of secondary data Report writing and presentation For more information about CCBR or the Institute for Economic Development, please contact (210) 458-2020. The mission of the Institute for Economic Development is to provide ongoing consulting, training, technical, research and information services in tandem with University-based assets and resources and other state, federal and local agencies, to facilitate economic, community and business development throughout South Texas and the Border Region. Working together to build the economy one business at a time. 31

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